
package com.heima.article.job;

import com.alibaba.fastjson.JSON;
import com.heima.article.service.HotArticleService;
import com.heima.common.constants.article.HotArticleConstants;
import com.heima.model.mess.app.ArticleVisitStreamMess;
import com.heima.model.mess.app.UpdateArticleMess;
import com.xxl.job.core.biz.model.ReturnT;
import com.xxl.job.core.handler.annotation.XxlJob;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.dao.DataAccessException;
import org.springframework.data.redis.connection.RedisConnection;
import org.springframework.data.redis.core.ListOperations;
import org.springframework.data.redis.core.RedisCallback;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.data.redis.serializer.RedisSerializer;
import org.springframework.stereotype.Component;
import org.springframework.util.CollectionUtils;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Optional;
import java.util.function.BinaryOperator;
import java.util.stream.Collectors;

@Component
@Slf4j
public class UpdateHotArticleJob {

    @Autowired
    StringRedisTemplate redisTemplate;
    @Autowired
    HotArticleService hotArticleService;



    @XxlJob("updateHotArticleJob")
    public ReturnT updateHotArticleHandler(String params){
        log.info("热文章分值更新 调度任务开始执行....");
        //1.获取redis行为列表中待处理数据
        List<UpdateArticleMess> articleMessList = getUpdateArticleMesses();
        if(CollectionUtils.isEmpty(articleMessList)){
            log.info("热文章分值更新：太冷清了 未产生任何文章行为调度任务完成...");
            return ReturnT.SUCCESS;
        }
        // 2. 将数据按照文章分组  进行聚合统计 得到待更新的数据列表
        List<ArticleVisitStreamMess> waitUpdateScoreData = getArticleVisitStreamMesses(articleMessList);
        if(CollectionUtils.isEmpty(waitUpdateScoreData)){
            log.info("热文章分值更新: 太冷清了 未产生任何文章行为 调度任务完成....");
            return ReturnT.SUCCESS;
        }
        // TODO 定时更新文章热度
        waitUpdateScoreData.forEach(hotArticleService::updateApArticle);
        log.info("热文章分值更新 调度任务完成....");
        return ReturnT.SUCCESS;
    }


    /**
     * 按文章分组  每个文章的所有行为 进行聚合处理
     * @param articleMessList 处理结果集合
     * @return
     */
    private List<ArticleVisitStreamMess> getArticleVisitStreamMesses(List<UpdateArticleMess> articleMessList) {
        List<ArticleVisitStreamMess> waitUpdateScoreData  = new ArrayList<>();
        //1 按照文章id分组，获取对应分组下的文章列表
        Map<Long, List<UpdateArticleMess>> map = articleMessList.stream().collect(Collectors.groupingBy(UpdateArticleMess::getArticleId));
        map.forEach((articleId,messList) ->{
            Optional<ArticleVisitStreamMess> reduceResult = messList.stream().map(articleMes ->{
                ArticleVisitStreamMess visitStreamMess = new ArticleVisitStreamMess();
                visitStreamMess.setArticleId(articleId);
                switch (articleMes.getType()){
                    case LIKES:
                        //设置点赞数量
                        visitStreamMess.setLike(articleMes.getAdd());
                        break;
                    case VIEWS:
                        // 设置 阅读数量
                        visitStreamMess.setView(articleMes.getAdd());
                        break;
                    case COMMENT:
                        // 设置 评论数量
                        visitStreamMess.setComment(articleMes.getAdd());
                        break;
                    case COLLECTION:
                        // 设置 收藏数量
                        visitStreamMess.setCollect(articleMes.getAdd());
                        break;
                }
                return visitStreamMess;
            }).reduce(new BinaryOperator<ArticleVisitStreamMess>() {
                @Override
                public ArticleVisitStreamMess apply(ArticleVisitStreamMess a1, ArticleVisitStreamMess a2) {
                    a1.setLike(a1.getLike() + a2.getLike());
                    a1.setView(a1.getView() + a2.getView());
                    a1.setComment(a1.getComment() + a2.getComment());
                    a1.setCollect(a1.getCollect() + a2.getCollect());
                    return a1;
                }
            });
            if(reduceResult.isPresent()){
                ArticleVisitStreamMess visitStreamMess = reduceResult.get();
                log.info("热点文章 聚合计算结果  ===>{}" , visitStreamMess);
                waitUpdateScoreData.add(visitStreamMess);
            }
        });
        return waitUpdateScoreData;
    }

    /**
     * 获取redis list列表中的待处理行为数据
     * @return
     */
    private List<UpdateArticleMess> getUpdateArticleMesses() {
        // 1. 获取redis 行为列表中待处理数据
        ListOperations listOperations = redisTemplate.opsForList();
        // 得到当前行为数据数量
        Long size = listOperations.size(HotArticleConstants.HOT_ARTICLE_SCORE_BEHAVIOR_LIST);
        // 采用管道命令， 让多个命令保证原子性
        List result = redisTemplate.executePipelined(new RedisCallback<List<UpdateArticleMess>>() {
            @Override
            public List<UpdateArticleMess> doInRedis(RedisConnection redisConnection) throws DataAccessException {
                //开启管道执行命令
                redisConnection.openPipeline();
                // 获取 0 到 size-1 的所有集合数据
                redisConnection.lRange(HotArticleConstants.HOT_ARTICLE_SCORE_BEHAVIOR_LIST.getBytes(),0,(size-1));
                // 截断 size 到 -1 后续的集合数据
                redisConnection.lTrim(HotArticleConstants.HOT_ARTICLE_SCORE_BEHAVIOR_LIST.getBytes(),size,-1);
                return null;
            }
        }, RedisSerializer.string());
        if(result.size()>0){
            List<String> listData = (List<String>) result.get(0);
            return listData.stream().map(str -> JSON.parseObject(str,UpdateArticleMess.class)).collect(Collectors.toList());
        }
        return null;
    } 
}